Predicting intrinsic clearance using deep learning-based drug-metabolic enzyme interaction features on an
Hyunjung Lee1, Hyeonseok Kang2, Jung-Woo Chae1
1Department of Bio-AI Convergence, Chungnam National University, Daejeon 34134, Republic of Korea; College of Pharmacy, Chungnam National University, Daejeon 34134, Republic of Korea.
Computer Methods and Programs in Biomedicine
|May 24, 2026
Summary
This study developed a computational framework to predict intrinsic clearance, a key pharmacokinetic parameter, by integrating drug-target interaction data with physicochemical properties. The approach shows potential for early-stage drug development by improving prediction accuracy when experimental data is limited.
Area of Science:
- Pharmacokinetics and Drug Metabolism
- Computational Chemistry and Cheminformatics
- Drug Discovery and Development
Background:
- Intrinsic clearance is crucial for systemic exposure and dose selection in drug development.
- Conventional in vitro-in vivo extrapolation (IVIVE) methods can be resource-intensive and challenging for early screening.
- A need exists for efficient computational tools to predict intrinsic clearance.
Purpose of the Study:
- To develop a biologically informed computational framework for predicting intrinsic clearance.
- To integrate drug-target interaction (DTI)-derived features with physicochemical properties.
- To utilize an IVIVE-based endpoint harmonization approach for unifying heterogeneous data.
Main Methods:
- Assembled and harmonized a multi-source human clearance dataset.
- Employed a pretrained DTI model (ChemBERTa, ProtBERT) to generate interaction features for hepatic metabolic proteins.
- Combined DTI features with compound descriptors in multilayer perceptron (MLP) and transformer encoder models.
- Evaluated models using cross-validation and an independent external dataset.
Main Results:
- DTI-derived features improved regression performance over descriptor-only models.
- The MLP model with DTI + LogP/Fup demonstrated the highest overall regression performance (r²m = 0.2505).
- On the external set, the MLP model achieved an AFE of 1.116, with over 70% of predictions within 3-fold error.
Conclusions:
- The developed framework aids intrinsic clearance estimation by integrating DTI and physicochemical data.
- The approach shows promise as a supportive tool for early-stage drug prioritization.
- Further research is needed to expand datasets and incorporate additional elimination pathways.
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